The invention relates to a novel neural network architecture implemented with materials that exhibit photoelectrical signals with characteristics similar to the molecule bacteriorhodopsin and related pigments. This unique neural network architecture uses such materials to implement a reprogrammable, highly parallel synapse matrix activated by light where, unlike other implementations, the current source lies in the synapse itself.
An additional essential feature of these types of materials is the ability to read in parallel with light a matrix of a certain molecular state without erasing such state.
Neural networks have been studied for many years as models for understanding information processing in the human brain. These studies have led researchers to see the potential of performing various artificial intelligence tasks with such associative memories. However, the technical implementation of neural networks is limited due to the absence of efficient and reliable hardware realizations of neural networks. This difficulty arises from the fundamental principles of neural networks which emphasize that massive parallel networks are needed for effective implementation of these powerful computing machines. The need for millions of reprogrammble pixels connected and activated in parallel is hard to obtain with common microelectronics hardware.
Basically the use of an electrooptic approach is more effective than the common methodologies of microelectronics when parallel behavior is needed. This is so because a light beam can act as a parallel activator of a network of connections.
Electronic and elecrooptic hardware implementations of neural networks have been reported (3-5). These implementations use microresistors or photoconductive elements to implement the synapses matrix. These elements are activated to produce a binary state in each synapse. The dimension of these elements is in the range of 30-100 microns and the use of binary logic decreases programming capabilities of the network. In addition, the complex activation of such networks and the limited reprogrammability is cumbersome. Thus these factors restrict the development of effective neural network devices.